Methodologies for analyzing the forces that move and shape national economies have advanced markedly in the last thirty years, enabling economists as never before to unite theoretical and empirical research and align measurement with theory. In <i>Structural Macroeconometrics</i>, David DeJong and...

We explore the implications of “nosy” preferences—when individuals have rankings over the specific actions of others—using recent theoretical results in the behavioral economics literature. Our model jointly captures preference interdependence over utilities (benevolence) and actions...

A foundational paradox in social choice theory is that liberalism (freedom of action) and Pareto efficiency, the standard in evaluating economic outcomes, can conflict with each other (Sen 1970). We capture this tension in a series of sequential Battle of the Sexes game experiments. We find that...

We examine the role of generalized constant gain stochastic gradient (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat tailed...

This paper investigates the relative importance of shocks to total factor productivity (TFP) versus the marginal efficiency of investment (MEI) in explaining cyclical variations. The literature offers contrasting results: TFP shocks are important in neoclassical environments, while relatively...

Methodologies for analyzing the forces that move and shape national economies have advanced markedly in the last thirty years, enabling economists as never before to unite theoretical and empirical research and align measurement with theory. In <i>Structural Macroeconometrics</i>, David DeJong and...

We examine the role of commercial banks in monetary transmission in a factor-augmented vector autoregression (FAVAR). A FAVAR exploits a large number of macroeconomic indicators to identify monetary policy shocks, and we add commonly used lending aggregates and lending data at the bank level....

Recursive least squares learning is a central concept employed in selecting amongst competing outcomes of dynamic stochastic economic models. In employing least squares estimators, such learning relies on the assumption of a symmetric loss function defined over estimation errors. Within a...